Shopping cart abandonment is considered the bane of Internet retailing and has received increasing attention in the retail industry. Most shopping cart abandonment metrics used in practice are based on purchase outcomes within a given web navigation session and ignore the fact that online consumers can and often do break up a given shopping task into multiple shopping sessions. In this study, we investigate online cart abandonment across multiple shopping sessions and examine its connections to consumers’ order and purchase spending decisions. We propose a joint model of cart, order, and spending decisions that takes into account their interdependencies within and across shopping sessions, and provide a method for accurately predicting the eventual shopping cart abandonment rate. We illustrate the application of our proposed methodology using data provided by an online grocery retailer and examine the effectiveness of shopping cart recovery promotions under different scenarios through simulation analyses.

Our study shows that consumers often break a shopping task into multiple shopping sessions, and that the eventual shopping cart abandonment rate is much lower when this is taken into consideration. We find strong interdependences between the cart and order decisions within and across shopping sessions as well as interesting interaction effects of the promotion level and time since the previous shopping session on cart, order, and spending decisions. For example, promotions increase old cart recovery within a certain time period since the previous shopping session, but encourage new cart usage afterward. Our simulation results indicate that shopping cart abandonment is not always harmful to retailers, and that they should take a holistic approach to designing retargeting email promotions and focus on increasing consumer spending instead of maximizing shopping cart recovery.

In recent year marketer’s attention has been drawn to the prominence of customer retention and the need to low churn, in particular of high value customers. Yet this thinking and analysis has largely focused on the case of a single product, while firms had been advised for a while to manage their offerings as platforms that consist of families of products with a common underlying logic. Taking a platform view, when a new product of the same family enters the market, the users of current products may be good candidates for acquisition efforts to the new offering. This raises interesting questions which widen the existing discussion of customer retention. Which customers would the firm want to transfer from current products and when? Would we want to churn the “best customers” of the current product earlier or later? How would considerations of value that stems from purchases (lifetime value) be different value that stems from the effect on others (social value)? We analyze these questions using agent based models, looking in particular at the market for mobile games, where the question of customer transfer among products is of high importance to firms.

Does the Opinion of the Crowd Predict Success? Evidence from Crowdsourcing

Ping Xiao
Assistant Professor of Marketing, Visiting Assistant Professor of Marketing
Business School of the National University of Singapore, NYU Shanghai

Li Wang
Assistant Professor of Marketing
Shanghai University of Finance and Economics

Noshir Contractor
Jane S. & William J. White Professor of Behavioral Sciences
McCormick School of Engineering & Applied Science, the School of Communication and the
Kellogg School of Management at Northwestern University

Abstract

“Crowdsourcing” is the sourcing of organizational functions from the “crowd”: a large, undefined community of a firm’s consumers, partners, and collaborators. A crucial challenge in crowdsourcing is to determine the quality of crowdsourced submissions. To help screen submissions, crowdsourcing portals use crowdvoting: they ask the community to vote on submissions. Our study investigates the informational role of crowdvoting on design submissions on Threadless, a pioneering crowdsourcing website. We collect and examine a novel, large scale dataset tracking over 150,000 designs, submitted by over 45,000 designers, voted on almost 150 million times, by over 600,000 different users. We focus on two questions. First, what is the conventional wisdom—how does crowdvoting influence Threadless? Second, does the conventional wisdom stand up to scrutiny—does crowdvoting systematically predict commercial success? We document several new empirical findings relating crowdvoting to revenues. We conclude by discussing the implications of our research for designers and firms seeking to ride the crowdsourcing tide.

Technology and Consumer Behavior

Electronic devices are assumed to make markets more efficient and to create a distribution channel for market information. These devices and the applications that run them allow people to engage in commercial transactions on the go, to access information from all parts of the globe, and to communicate through voice and video from anywhere. In the series of papers that make up this talk I will show that people's interactions with these devices can evoke psychological processes that influence the judgments and decisions that people make when using them. Specifically, in multiple field and lab studies I examine how the physical interaction with electronic devices influences psychological processes in systematic ways.

By Hannes DATTA
Joint work with George Knox and Bart Bronnenberg (both Tilburg University)

Digital streaming is set to take over as the dominant business model in industries like music (e.g., Spotify), movies (e.g., Netflix), books (e.g., Kindle Unlimited), and games (e.g., Steam). Instead of purchasing individual content, streaming allows users to rent access to a vast library of digital content that is free at the margin. Using a panel data set of individual consumers’ listening histories across many platforms, we study how the shift from purchasing to streaming affects society.

Prior work has established that the adoption of online streaming leads to a sizeable effects at the individual level. For example, consumers discover more new content, and tend to favor less popular artists over superstars. However, it is not clear how online streaming affects consumption behavior at the societal level (i.e., across consumers). On the one hand, consumer tastes may become fragmented when choosing among less popular and newer artists. On the other hand, consumer tastes may become more homogenous if recommendation systems and curated playlists on streaming services push users to the same new content.

From a public policy perspective, too much fragmentation is bad news because it can diminish social capital, as fewer people share the same experience. However, too little fragmentation may signal a lack of diversity, favoring superstars and damaging independent labels. We examine several possible drivers of fragmentation, and use our data to test competing explanations.

Valuing Non-Contractual Firms Using Common Customer Metrics

Marketing

Intervenant :
Daniel McCarthy
Doctorant Départment de Statistiques
,
Wharton School of the University of Pennsylvania

3 mars 2017
-
Salle T04
- De 10h30
à 12h00

Valuing Non-Contractual Firms Using Common Customer Metrics

There is growing interest in the notion of “customer-based corporate valuation,” explicitly tying the value of a firm's customers to the firm's overall financial valuation. While much progress has been made in building a well-validated customer-based valuation model for contractual (or subscription-based) firms, there has been less progress for non-contractual firms (e.g., retail, travel/hospitality, and mobile gaming). Non-contractual businesses have more complex transactional patterns than contractual ones for a variety of reasons, including (1) they are characterized by latent attrition instead of observable churn behavior, (2) they often have irregular purchase incidence timing and spend amounts. These factors make it harder to reconstruct granular purchase behaviors from aggregate data, and to understand what metrics would serve as the best inputs for such a model. Despite this lack of guidance, a number of non-contractual firms regularly report a variety of different aggregate measures to their shareholders (e.g., the number of active users). We use a novel methodology based upon “indirect inference,” a well-established generalization of generalized method-of-moments procedures, to draw a connection between these common aggregate metrics and the underlying parameters of latent variable models for repeat purchasing. We show how the overall predictive validity of the models varies as a function of the combination of metrics used to train the models; this allows us to better understand both how many and which metrics are needed to achieve adequate predictions of future revenues. We apply this methodology to quarterly data from the largest subsidiary of an e-commerce retailer, valuing the subsidiary as a whole, decomposing this valuation into existing and yet-to-be-acquired customers, and analyzing the profitability of newly-acquired customers.